##        IDjoueur    nom_du_joueur heure_connexion_joueur nom_du_jeu
##    1: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    2: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    3: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    4: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##    5: 05ezh8lfl Sandrine Bruneau   12_20_2016_15h14m33s   Logique2
##   ---                                                             
## 5042: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5043: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5044: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5045: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
## 5046: zv35u39vc   Nadège BELLEC   12_20_2016_12h33m13s    Motrice
##       modeTest mise_first_1 action_de_jeu duree_tour_ms mise confiance
##    1:        0            1             2         43967    3        50
##    2:        0            1             3         27818    6        70
##    3:        0            1             4         18807    7       100
##    4:        0            1             5         24111    7       100
##    5:        0            1             6         30515    7       100
##   ---                                                                 
## 5042:        0            0            26          6363    7       100
## 5043:        0            0            27          6401    1        10
## 5044:        0            0            28          7363    2        30
## 5045:        0            0            29          6833    2        30
## 5046:        0            0            30          8730    1        20
##       difficulty gameDiff near_miss moutons_sauves moutons_tues score
##    1:       0.41     5.00         0             10            0    10
##    2:       0.46     5.00         0             16            0    16
##    3:       0.17     2.00         0             23            0    23
##    4:       0.24     3.00         0             30            0    30
##    5:       0.45     5.00         0             37            0    37
##   ---                                                                
## 5042:       0.20     3.60        17             60           59     1
## 5043:       0.87     8.96       -28             60           60     0
## 5044:       0.36     4.88       -10             62           60     2
## 5045:       0.55     6.40       -16             62           62     0
## 5046:       0.93     9.44        52             62           63    -1
##       gagnant          horodateur        prenomNom age sexe
##    1:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    2:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    3:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    4:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##    5:       1 12/20/2016 15:20:04 Sandrine Bruneau  46    1
##   ---                                                      
## 5042:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5043:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5044:       1 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5045:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
## 5046:       0 12/20/2016 12:37:14    Nad<U+008A>ge BELLEC  38    1
##       langueMaternelle niveauEtude
##    1:                1           7
##    2:                1           7
##    3:                1           7
##    4:                1           7
##    5:                1           7
##   ---                             
## 5042:                1           4
## 5043:                1           4
## 5044:                1           4
## 5045:                1           4
## 5046:                1           4
##                                              jeuxFav autoEffJoueur1
##    1:                                       pacman_              NA
##    2:                                       pacman_              NA
##    3:                                       pacman_              NA
##    4:                                       pacman_              NA
##    5:                                       pacman_              NA
##   ---                                                              
## 5042: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5043: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5044: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5045: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
## 5046: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID             NA
##       autoEffJoueur2 autoEffJoueur3 autoEffJoueur4 autoEffJoueur5
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 5042:             NA             NA             NA             NA
## 5043:             NA             NA             NA             NA
## 5044:             NA             NA             NA             NA
## 5045:             NA             NA             NA             NA
## 5046:             NA             NA             NA             NA
##       autoEffJoueur6 autoEffJoueur7 autoEffJoueur8 autoEffJoueur9
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 5042:             NA             NA             NA             NA
## 5043:             NA             NA             NA             NA
## 5044:             NA             NA             NA             NA
## 5045:             NA             NA             NA             NA
## 5046:             NA             NA             NA             NA
##       autoEffJoueur10 loterie1 loterie2 loterie3 loterie4 loterie5
##    1:              NA        1        1        1        1        0
##    2:              NA        1        1        1        1        0
##    3:              NA        1        1        1        1        0
##    4:              NA        1        1        1        1        0
##    5:              NA        1        1        1        1        0
##   ---                                                             
## 5042:              NA        1        1        0        0        1
## 5043:              NA        1        1        0        0        1
## 5044:              NA        1        1        0        0        1
## 5045:              NA        1        1        0        0        1
## 5046:              NA        1        1        0        0        1
##       loterie6 loterie7 loterie8 loterie9 loterie10 profilJoueur8
##    1:        0        1        1        1         1             0
##    2:        0        1        1        1         1             0
##    3:        0        1        1        1         1             0
##    4:        0        1        1        1         1             0
##    5:        0        1        1        1         1             0
##   ---                                                            
## 5042:        1        1        1        1         1             0
## 5043:        1        1        1        1         1             0
## 5044:        1        1        1        1         1             0
## 5045:        1        1        1        1         1             0
## 5046:        1        1        1        1         1             0
##       play.video.games play.board.games play.money.games self.eff
##    1:              0.4              0.2              0.8       NA
##    2:              0.4              0.2              0.8       NA
##    3:              0.4              0.2              0.8       NA
##    4:              0.4              0.2              0.8       NA
##    5:              0.4              0.2              0.8       NA
##   ---                                                            
## 5042:              1.0              0.4              0.4       NA
## 5043:              1.0              0.4              0.4       NA
## 5044:              1.0              0.4              0.4       NA
## 5045:              1.0              0.4              0.4       NA
## 5046:              1.0              0.4              0.4       NA
## Nombre de participants à l'expérimentation :  58
## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of participants :  58"
## [1] "Total number of outliers:  4"
## [1] "- total number of outliers motor task:  2"
## [1] "- total number of outliers perceptive task:  1"
## [1] "- total number of outliers logical task:  1"
## [1] "Total number of participants after removing outliers:  58"
## [1] "- motor:  56"
## [1] "- perceptive:  57"
## [1] "- logical:  57"

Modeling difficulty

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1953.7   1975.3   -972.8   1945.7     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1396 -0.7500  0.2888  0.7385  2.8481 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5631   0.7504  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0298     0.1873  -5.499 3.83e-08 ***
## difficulty    2.9618     0.2146  13.803  < 2e-16 ***
## timeNorm     -0.5280     0.2020  -2.614  0.00895 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.539       
## timeNorm   -0.571 -0.009
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1624         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.050110  
##  1st Qu.:-0.438217  
##  Median :-0.118832  
##  Mean   :-0.002364  
##  3rd Qu.: 0.296005  
##  Max.   : 1.658440  
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1287.3   1308.9   -639.6   1279.3     1649 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3813 -0.3588  0.1128  0.3561  6.6474 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7479   0.8648  
## Number of obs: 1653, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.3420     0.2577 -12.967   <2e-16 ***
## difficulty    8.2722     0.4031  20.520   <2e-16 ***
## timeNorm     -0.3164     0.2646  -1.196    0.232    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.651       
## timeNorm   -0.516 -0.045
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0968078 (tol =
## 0.001, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0968078 (tol = 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1653 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.6740621  
##  1st Qu.:-0.4631746  
##  Median : 0.0733813  
##  Mean   :-0.0008036  
##  3rd Qu.: 0.4485664  
##  Max.   : 1.5533202  
## [1] "Intercept: -3.34 1.9e-38 ***"
## [1] "Difficulty: 8.27 1.4e-93 ***"
## [1] "Time: -0.316 0.23 :("
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"
##          0%         25%         50%         75%        100% 
## -1.55332020 -0.44856637 -0.07338126  0.46317464  1.67406213

##          0%         25%         50%         75%        100% 
## -1.55332020 -0.44856637 -0.07338126  0.46317464  1.67406213

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1552.8   1574.4   -772.4   1544.8     1649 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.0811 -0.4934 -0.1180  0.4990  5.2065 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.53     1.237   
## Number of obs: 1653, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.7716     0.2500  -7.087 1.37e-12 ***
## difficulty    5.7158     0.3070  18.615  < 2e-16 ***
## timeNorm     -2.1395     0.2486  -8.608  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.487       
## timeNorm   -0.373 -0.253
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1653         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.8176657  
##  1st Qu.:-0.7404031  
##  Median :-0.2056618  
##  Mean   :-0.0000472  
##  3rd Qu.: 0.7132065  
##  Max.   : 3.1485721  
## [1] "Intercept: -1.77 1.4e-12 ***"
## [1] "Difficulty: 5.72 2.4e-77 ***"
## [1] "Time: -2.14 7.5e-18 ***"
## [1] "R2 fixed: 0.39"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1600"
##         0%        25%        50%        75%       100% 
## -3.1485721 -0.7132065  0.2056618  0.7404031  1.8176657

##         0%        25%        50%        75%       100% 
## -3.1485721 -0.7132065  0.2056618  0.7404031  1.8176657

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1442117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.5689, p-value = 0.5694
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05907689

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.36057, p-value = 0.7184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0374431

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08913015

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.19027, p-value = 0.8491
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01944679

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.75722, p-value = 0.4489
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07770109

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.7713, p-value = 0.005584
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3708505 
## 
## [1] "self.eff.on.level.s 0.37 0.0056 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.69753, p-value = 0.4855
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09334332

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1554335

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8306, p-value = 0.06716
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1794643 
## 
## [1] "risk.av.on.level.s 0.18 0.067 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.175, p-value = 0.24
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1154221

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09369113
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2707, p-value = 0.02317
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2167271 
## 
## [1] "age.on.level.s 0.22 0.023 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1924, p-value = 0.2331
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1137751

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2377395 
## 
## [1] "sexe.on.level.m -0.24 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.22007, p-value = 0.8258
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02422079

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.20601, p-value = 0.8368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0226739

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.82775747 -0.05457213
## sample estimates:
## difference in location 
##             -0.4558716 
## 
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 348, p-value = 0.8339
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4740139  0.4341707
## sample estimates:
## difference in location 
##             -0.0477841

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 339, p-value = 0.845
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7708044  0.5990311
## sample estimates:
## difference in location 
##            -0.02530146

CONFIDENCE SCALE APPROACH

For Bet approach, see the other file.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0084 51     0.73 :(
##  2:      0.09375         0.0250 57     0.14 :(
##  3:      0.15625        -0.0130 57     0.44 :(
##  4:      0.21875         0.0430 58     0.14 :(
##  5:      0.28125        -0.0430 57     0.19 :(
##  6:      0.34375         0.0015 57     0.96 :(
##  7:      0.40625         0.0220 56     0.45 :(
##  8:      0.46875        -0.0220 57     0.56 :(
##  9:      0.53125         0.0044 55     0.98 :(
## 10:      0.59375        -0.0100 58     0.77 :(
## 11:      0.65625        -0.0640 58     0.035 *
## 12:      0.71875        -0.1100 58 2.5e-05 ***
## 13:      0.78125        -0.1500 56 3.7e-08 ***
## 14:      0.84375        -0.1900 56 3.9e-09 ***
## 15:      0.90625        -0.2000 57 4.9e-11 ***
## 16:      0.96875        -0.1700 57 4.9e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 51     0.73 :(
##  2: 57     0.14 :(
##  3: 57     0.44 :(
##  4: 58     0.14 :(
##  5: 57     0.19 :(
##  6: 57     0.96 :(
##  7: 56     0.45 :(
##  8: 57     0.56 :(
##  9: 55     0.98 :(
## 10: 58     0.77 :(
## 11: 58     0.035 *
## 12: 58 2.5e-05 ***
## 13: 56 3.7e-08 ***
## 14: 56 3.9e-09 ***
## 15: 57 4.9e-11 ***
## 16: 57 4.9e-11 ***
## [1] 56.6
## [1] 1.71

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 36     0.83 :(
##  2:      0.09375        -0.0045 38     0.88 :(
##  3:      0.15625        -0.0670 44      0.2 :(
##  4:      0.21875         0.0130 42     0.74 :(
##  5:      0.28125        -0.0430 40     0.36 :(
##  6:      0.34375         0.0130 39     0.74 :(
##  7:      0.40625         0.0650 42     0.15 :(
##  8:      0.46875         0.0310 39      0.7 :(
##  9:      0.53125        -0.0220 40     0.91 :(
## 10:      0.59375        -0.0220 43     0.53 :(
## 11:      0.65625        -0.0780 36     0.045 *
## 12:      0.71875        -0.1500 39 0.00023 ***
## 13:      0.78125        -0.1800 38 0.00015 ***
## 14:      0.84375        -0.2400 27 1.7e-05 ***
## 15:      0.90625        -0.1900 31 1.1e-06 ***
## 16:      0.96875        -0.1500 20 7.5e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 36     0.83 :(
##  2: 38     0.88 :(
##  3: 44      0.2 :(
##  4: 42     0.74 :(
##  5: 40     0.36 :(
##  6: 39     0.74 :(
##  7: 42     0.15 :(
##  8: 39      0.7 :(
##  9: 40     0.91 :(
## 10: 43     0.53 :(
## 11: 36     0.045 *
## 12: 39 0.00023 ***
## 13: 38 0.00015 ***
## 14: 27 1.7e-05 ***
## 15: 31 1.1e-06 ***
## 16: 20 7.5e-05 ***
## [1] 37.1
## [1] 6.29

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 29     0.18 :(
##  2:      0.09375          0.040 33     0.089 .
##  3:      0.15625          0.058 31     0.61 :(
##  4:      0.21875          0.013 38     0.98 :(
##  5:      0.28125         -0.067 36     0.27 :(
##  6:      0.34375         -0.058 36     0.34 :(
##  7:      0.40625         -0.025 35     0.56 :(
##  8:      0.46875         -0.040 35     0.38 :(
##  9:      0.53125          0.088 36     0.072 .
## 10:      0.59375          0.073 35     0.19 :(
## 11:      0.65625         -0.067 36      0.2 :(
## 12:      0.71875         -0.076 38      0.1 :(
## 13:      0.78125         -0.067 38     0.018 *
## 14:      0.84375         -0.130 37 3.4e-05 ***
## 15:      0.90625         -0.190 34 3.6e-07 ***
## 16:      0.96875         -0.180 32 8.2e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 29     0.18 :(
##  2: 33     0.089 .
##  3: 31     0.61 :(
##  4: 38     0.98 :(
##  5: 36     0.27 :(
##  6: 36     0.34 :(
##  7: 35     0.56 :(
##  8: 35     0.38 :(
##  9: 36     0.072 .
## 10: 35     0.19 :(
## 11: 36      0.2 :(
## 12: 38      0.1 :(
## 13: 38     0.018 *
## 14: 37 3.4e-05 ***
## 15: 34 3.6e-07 ***
## 16: 32 8.2e-07 ***
## [1] 34.9
## [1] 2.59

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  1          NA
##  2:      0.09375        -0.0220  9     0.72 :(
##  3:      0.15625        -0.0041 12        1 :(
##  4:      0.21875        -0.0045 13     0.52 :(
##  5:      0.28125         0.1000 12     0.32 :(
##  6:      0.34375         0.1400 11     0.12 :(
##  7:      0.40625         0.0760 14     0.29 :(
##  8:      0.46875        -0.0760 17     0.42 :(
##  9:      0.53125        -0.1000 15     0.27 :(
## 10:      0.59375        -0.1100 16     0.17 :(
## 11:      0.65625        -0.1000 17     0.11 :(
## 12:      0.71875        -0.1100 16     0.052 .
## 13:      0.78125        -0.1700 17   0.0024 **
## 14:      0.84375        -0.1800 20   0.0042 **
## 15:      0.90625        -0.1600 20 9.4e-05 ***
## 16:      0.96875        -0.2400 20 9.4e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.72 :(
##  2: 12        1 :(
##  3: 13     0.52 :(
##  4: 12     0.32 :(
##  5: 11     0.12 :(
##  6: 14     0.29 :(
##  7: 17     0.42 :(
##  8: 15     0.27 :(
##  9: 16     0.17 :(
## 10: 17     0.11 :(
## 11: 16     0.052 .
## 12: 17   0.0024 **
## 13: 20   0.0042 **
## 14: 20 9.4e-05 ***
## 15: 20 9.4e-05 ***
## [1] 15.3
## [1] 3.41
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375         -0.094  8     0.21 :(
##  3:      0.15625         -0.099 26     0.015 *
##  4:      0.21875         -0.076 40   0.0065 **
##  5:      0.28125         -0.067 45     0.055 .
##  6:      0.34375         -0.058 47     0.21 :(
##  7:      0.40625         -0.013 49      0.8 :(
##  8:      0.46875          0.031 49     0.73 :(
##  9:      0.53125          0.076 51     0.15 :(
## 10:      0.59375          0.025 51     0.55 :(
## 11:      0.65625         -0.013 53     0.45 :(
## 12:      0.71875         -0.052 51     0.079 .
## 13:      0.78125         -0.067 44     0.029 *
## 14:      0.84375         -0.094 27   0.0073 **
## 15:      0.90625         -0.078 14 0.00076 ***
## 16:      0.96875         -0.110  6     0.034 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.21 :(
##  2: 26     0.015 *
##  3: 40   0.0065 **
##  4: 45     0.055 .
##  5: 47     0.21 :(
##  6: 49      0.8 :(
##  7: 49     0.73 :(
##  8: 51     0.15 :(
##  9: 51     0.55 :(
## 10: 53     0.45 :(
## 11: 51     0.079 .
## 12: 44     0.029 *
## 13: 27   0.0073 **
## 14: 14 0.00076 ***
## 15:  6     0.034 *
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n     pval
##  1:      0.03125             NA  0       NA
##  2:      0.09375        -0.0940  8  0.21 :(
##  3:      0.15625        -0.1200 24 0.005 **
##  4:      0.21875        -0.0760 26  0.031 *
##  5:      0.28125        -0.0670 25  0.12 :(
##  6:      0.34375         0.0130 26   0.8 :(
##  7:      0.40625         0.0320 25  0.67 :(
##  8:      0.46875         0.0880 24  0.14 :(
##  9:      0.53125         0.0760 23  0.21 :(
## 10:      0.59375         0.0970 24  0.038 *
## 11:      0.65625         0.0081 25  0.94 :(
## 12:      0.71875        -0.0470 22  0.078 .
## 13:      0.78125        -0.1000 15  0.26 :(
## 14:      0.84375             NA  0       NA
## 15:      0.90625             NA  0       NA
## 16:      0.96875             NA  0       NA
## [1] "mean and sd of nb players per bin"
##     nb     pval
##  1:  8  0.21 :(
##  2: 24 0.005 **
##  3: 26  0.031 *
##  4: 25  0.12 :(
##  5: 26   0.8 :(
##  6: 25  0.67 :(
##  7: 24  0.14 :(
##  8: 23  0.21 :(
##  9: 24  0.038 *
## 10: 25  0.94 :(
## 11: 22  0.078 .
## 12: 15  0.26 :(
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625         0.2000  2      1 :(
##  4:      0.21875        -0.2200 14   0.15 :(
##  5:      0.28125        -0.0990 20   0.38 :(
##  6:      0.34375        -0.1600 20    0.08 .
##  7:      0.40625        -0.0490 22   0.31 :(
##  8:      0.46875        -0.0160 21   0.63 :(
##  9:      0.53125         0.1400 21 0.0048 **
## 10:      0.59375         0.0130 21   0.86 :(
## 11:      0.65625        -0.0130 21   0.94 :(
## 12:      0.71875         0.0430 22   0.43 :(
## 13:      0.78125        -0.0099 21   0.75 :(
## 14:      0.84375        -0.0940 19   0.017 *
## 15:      0.90625             NA  6        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.15 :(
##  3: 20   0.38 :(
##  4: 20    0.08 .
##  5: 22   0.31 :(
##  6: 21   0.63 :(
##  7: 21 0.0048 **
##  8: 21   0.86 :(
##  9: 21   0.94 :(
## 10: 22   0.43 :(
## 11: 21   0.75 :(
## 12: 19   0.017 *
## [1] 18.7
## [1] 5.66
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625         -0.049 2    1 :(
##  8:      0.46875         -0.180 4 0.58 :(
##  9:      0.53125         -0.400 7 0.071 .
## 10:      0.59375         -0.290 6 0.14 :(
## 11:      0.65625         -0.230 7 0.16 :(
## 12:      0.71875         -0.250 7 0.047 *
## 13:      0.78125         -0.180 8 0.023 *
## 14:      0.84375         -0.110 8 0.29 :(
## 15:      0.90625         -0.110 8 0.013 *
## 16:      0.96875         -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  2    1 :(
##  2:  4 0.58 :(
##  3:  7 0.071 .
##  4:  6 0.14 :(
##  5:  7 0.16 :(
##  6:  7 0.047 *
##  7:  8 0.023 *
##  8:  8 0.29 :(
##  9:  8 0.013 *
## 10:  6 0.034 *
## [1] 6.3
## [1] 1.95
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 45     0.025 *
##  2:      0.09375         -0.094 54      0.01 *
##  3:      0.15625         -0.085 50     0.027 *
##  4:      0.21875         -0.040 41     0.18 :(
##  5:      0.28125         -0.067 40     0.35 :(
##  6:      0.34375         -0.094 38     0.15 :(
##  7:      0.40625          0.022 37     0.69 :(
##  8:      0.46875         -0.110 37     0.029 *
##  9:      0.53125         -0.140 30     0.045 *
## 10:      0.59375         -0.190 37   0.0051 **
## 11:      0.65625         -0.160 36     0.016 *
## 12:      0.71875         -0.180 35    0.002 **
## 13:      0.78125         -0.170 38 0.00063 ***
## 14:      0.84375         -0.140 46   5e-05 ***
## 15:      0.90625         -0.170 54 1.2e-10 ***
## 16:      0.96875         -0.140 57 4.3e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 45     0.025 *
##  2: 54      0.01 *
##  3: 50     0.027 *
##  4: 41     0.18 :(
##  5: 40     0.35 :(
##  6: 38     0.15 :(
##  7: 37     0.69 :(
##  8: 37     0.029 *
##  9: 30     0.045 *
## 10: 37   0.0051 **
## 11: 36     0.016 *
## 12: 35    0.002 **
## 13: 38 0.00063 ***
## 14: 46   5e-05 ***
## 15: 54 1.2e-10 ***
## 16: 57 4.3e-11 ***
## [1] 42.2
## [1] 7.93

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 20     0.57 :(
##  2:      0.09375        -0.0940 19   0.0012 **
##  3:      0.15625        -0.1600 18      0.04 *
##  4:      0.21875        -0.0045 11     0.39 :(
##  5:      0.28125        -0.0670 17     0.44 :(
##  6:      0.34375        -0.2000 12     0.037 *
##  7:      0.40625        -0.0490 13     0.48 :(
##  8:      0.46875        -0.2500 15     0.011 *
##  9:      0.53125        -0.3000 11     0.027 *
## 10:      0.59375        -0.3100 14   0.0096 **
## 11:      0.65625        -0.2300 14     0.038 *
## 12:      0.71875        -0.3600 12   0.0025 **
## 13:      0.78125        -0.3200 12     0.011 *
## 14:      0.84375        -0.2200 14   0.0082 **
## 15:      0.90625        -0.1600 19 0.00013 ***
## 16:      0.96875        -0.1500 20 8.1e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 20     0.57 :(
##  2: 19   0.0012 **
##  3: 18      0.04 *
##  4: 11     0.39 :(
##  5: 17     0.44 :(
##  6: 12     0.037 *
##  7: 13     0.48 :(
##  8: 15     0.011 *
##  9: 11     0.027 *
## 10: 14   0.0096 **
## 11: 14     0.038 *
## 12: 12   0.0025 **
## 13: 12     0.011 *
## 14: 14   0.0082 **
## 15: 19 0.00013 ***
## 16: 20 8.1e-05 ***
## [1] 15.1
## [1] 3.28

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 25   0.0082 **
##  2:      0.09375         -0.094 27     0.31 :(
##  3:      0.15625         -0.120 22     0.041 *
##  4:      0.21875         -0.040 22     0.24 :(
##  5:      0.28125         -0.067 16     0.77 :(
##  6:      0.34375         -0.022 21        1 :(
##  7:      0.40625          0.022 19     0.51 :(
##  8:      0.46875         -0.110 17     0.25 :(
##  9:      0.53125         -0.100 15     0.44 :(
## 10:      0.59375         -0.150 16     0.31 :(
## 11:      0.65625         -0.160 17     0.17 :(
## 12:      0.71875         -0.076 16     0.15 :(
## 13:      0.78125         -0.067 21     0.11 :(
## 14:      0.84375         -0.130 24   0.0066 **
## 15:      0.90625         -0.190 27 4.7e-06 ***
## 16:      0.96875         -0.140 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25   0.0082 **
##  2: 27     0.31 :(
##  3: 22     0.041 *
##  4: 22     0.24 :(
##  5: 16     0.77 :(
##  6: 21        1 :(
##  7: 19     0.51 :(
##  8: 17     0.25 :(
##  9: 15     0.44 :(
## 10: 16     0.31 :(
## 11: 17     0.17 :(
## 12: 16     0.15 :(
## 13: 21     0.11 :(
## 14: 24   0.0066 **
## 15: 27 4.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 20.8
## [1] 4.33

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0220  8   0.94 :(
##  3:      0.15625         0.0220 10   0.61 :(
##  4:      0.21875         0.0260  8      1 :(
##  5:      0.28125        -0.0670  7   0.93 :(
##  6:      0.34375        -0.0220  5   0.78 :(
##  7:      0.40625         0.1200  5   0.44 :(
##  8:      0.46875         0.2100  5   0.19 :(
##  9:      0.53125         0.0760  4   0.88 :(
## 10:      0.59375        -0.1700  7   0.55 :(
## 11:      0.65625        -0.0130  5   0.78 :(
## 12:      0.71875         0.0063  7      1 :(
## 13:      0.78125        -0.2100  5   0.19 :(
## 14:      0.84375        -0.0580  8   0.29 :(
## 15:      0.90625        -0.1700  8   0.014 *
## 16:      0.96875        -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.94 :(
##  2: 10   0.61 :(
##  3:  8      1 :(
##  4:  7   0.93 :(
##  5:  5   0.78 :(
##  6:  5   0.44 :(
##  7:  5   0.19 :(
##  8:  4   0.88 :(
##  9:  7   0.55 :(
## 10:  5   0.78 :(
## 11:  7      1 :(
## 12:  5   0.19 :(
## 13:  8   0.29 :(
## 14:  8   0.014 *
## 15: 10 0.0059 **
## [1] 6.8
## [1] 1.9
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 40     0.95 :(
##  2:      0.09375         0.1200 44     0.015 *
##  3:      0.15625         0.0580 46     0.19 :(
##  4:      0.21875         0.1900 48   0.0019 **
##  5:      0.28125         0.1100 36      0.2 :(
##  6:      0.34375         0.0850 44     0.078 .
##  7:      0.40625         0.0560 43     0.11 :(
##  8:      0.46875        -0.0045 42      0.9 :(
##  9:      0.53125        -0.0310 42     0.45 :(
## 10:      0.59375        -0.0220 46     0.94 :(
## 11:      0.65625        -0.0850 40     0.11 :(
## 12:      0.71875        -0.1500 44     0.016 *
## 13:      0.78125        -0.1400 47 0.00089 ***
## 14:      0.84375        -0.2700 48 1.5e-08 ***
## 15:      0.90625        -0.2400 43 1.1e-08 ***
## 16:      0.96875        -0.3000 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 40     0.95 :(
##  2: 44     0.015 *
##  3: 46     0.19 :(
##  4: 48   0.0019 **
##  5: 36      0.2 :(
##  6: 44     0.078 .
##  7: 43     0.11 :(
##  8: 42      0.9 :(
##  9: 42     0.45 :(
## 10: 46     0.94 :(
## 11: 40     0.11 :(
## 12: 44     0.016 *
## 13: 47 0.00089 ***
## 14: 48 1.5e-08 ***
## 15: 43 1.1e-08 ***
## 16: 29 2.7e-06 ***
## [1] 42.6
## [1] 4.83

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 28     0.75 :(
##  2:      0.09375         0.0490 28     0.33 :(
##  3:      0.15625         0.0580 27     0.69 :(
##  4:      0.21875         0.1900 26   0.0095 **
##  5:      0.28125        -0.0074 18        1 :(
##  6:      0.34375         0.0340 24     0.56 :(
##  7:      0.40625         0.1700 22     0.034 *
##  8:      0.46875         0.1000 21     0.25 :(
##  9:      0.53125        -0.0310 23     0.66 :(
## 10:      0.59375        -0.0760 24     0.15 :(
## 11:      0.65625        -0.1200 17     0.042 *
## 12:      0.71875        -0.1100 21      0.07 .
## 13:      0.78125        -0.1700 23     0.019 *
## 14:      0.84375        -0.2700 21 0.00015 ***
## 15:      0.90625        -0.2600 16 0.00046 ***
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28     0.75 :(
##  2: 28     0.33 :(
##  3: 27     0.69 :(
##  4: 26   0.0095 **
##  5: 18        1 :(
##  6: 24     0.56 :(
##  7: 22     0.034 *
##  8: 21     0.25 :(
##  9: 23     0.66 :(
## 10: 24     0.15 :(
## 11: 17     0.042 *
## 12: 21      0.07 .
## 13: 23     0.019 *
## 14: 21 0.00015 ***
## 15: 16 0.00046 ***
## [1] 22.6
## [1] 3.76
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 11     0.82 :(
##  2:      0.09375          0.330 15     0.011 *
##  3:      0.15625          0.240 16     0.052 .
##  4:      0.21875          0.210 16     0.027 *
##  5:      0.28125          0.100 12     0.25 :(
##  6:      0.34375          0.085 12     0.29 :(
##  7:      0.40625         -0.085 13     0.67 :(
##  8:      0.46875         -0.040 12     0.55 :(
##  9:      0.53125         -0.016 11        1 :(
## 10:      0.59375          0.190 13     0.023 *
## 11:      0.65625         -0.085 12     0.56 :(
## 12:      0.71875         -0.290 15     0.062 .
## 13:      0.78125         -0.100 15      0.1 :(
## 14:      0.84375         -0.220 17 0.00091 ***
## 15:      0.90625         -0.280 16 0.00048 ***
## 16:      0.96875         -0.330 16 0.00048 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.82 :(
##  2: 15     0.011 *
##  3: 16     0.052 .
##  4: 16     0.027 *
##  5: 12     0.25 :(
##  6: 12     0.29 :(
##  7: 13     0.67 :(
##  8: 12     0.55 :(
##  9: 11        1 :(
## 10: 13     0.023 *
## 11: 12     0.56 :(
## 12: 15     0.062 .
## 13: 15      0.1 :(
## 14: 17 0.00091 ***
## 15: 16 0.00048 ***
## 16: 16 0.00048 ***
## [1] 13.9
## [1] 2.06

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  1          NA
##  2:      0.09375             NA  1          NA
##  3:      0.15625             NA  3          NA
##  4:      0.21875         -0.040  6     0.83 :(
##  5:      0.28125          0.150  6     0.14 :(
##  6:      0.34375          0.320  8     0.079 .
##  7:      0.40625          0.022  8     0.44 :(
##  8:      0.46875         -0.040  9     0.34 :(
##  9:      0.53125         -0.100  8     0.44 :(
## 10:      0.59375         -0.170  9     0.41 :(
## 11:      0.65625          0.022 11     0.96 :(
## 12:      0.71875          0.001  8        1 :(
## 13:      0.78125         -0.140  9     0.12 :(
## 14:      0.84375         -0.340 10     0.014 *
## 15:      0.90625         -0.180 11   0.0038 **
## 16:      0.96875         -0.320 12 0.00049 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  6     0.83 :(
##  2:  6     0.14 :(
##  3:  8     0.079 .
##  4:  8     0.44 :(
##  5:  9     0.34 :(
##  6:  8     0.44 :(
##  7:  9     0.41 :(
##  8: 11     0.96 :(
##  9:  8        1 :(
## 10:  9     0.12 :(
## 11: 10     0.014 *
## 12: 11   0.0038 **
## 13: 12 0.00049 ***
## [1] 8.85
## [1] 1.82
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70319  -0.16766   0.00799   0.17682   0.64502  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17379    0.01995   8.711   <2e-16 ***
## timeNorm     0.00431    0.02101   0.205    0.837    
## obj.diff    -0.37273    0.02619 -14.234   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05419176)
## 
##     Null deviance: 99.193  on 1623  degrees of freedom
## Residual deviance: 87.845  on 1621  degrees of freedom
## AIC: -120.62
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77891  -0.19889  -0.04038   0.24162   0.77963  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10459    0.01719   6.086 1.44e-09 ***
## timeNorm     0.04073    0.02300   1.771   0.0768 .  
## obj.diff    -0.36296    0.01778 -20.408  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06800604)
## 
##     Null deviance: 140.80  on 1652  degrees of freedom
## Residual deviance: 112.21  on 1650  degrees of freedom
## AIC: 252.48
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72757  -0.18824  -0.01492   0.18450   0.70120  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22493    0.01907   11.80   <2e-16 ***
## timeNorm     0.07505    0.02360    3.18   0.0015 ** 
## obj.diff    -0.55871    0.02005  -27.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06619562)
## 
##     Null deviance: 169.02  on 1652  degrees of freedom
## Residual deviance: 109.22  on 1650  degrees of freedom
## AIC: 207.88
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.5348214     0.6008109 -0.0627956484 112 0.021 *
##  2:      4.5      0.5291667     0.5714407 -0.0363491910 168 0.071 .
##  3:      7.5      0.5071429     0.5416953 -0.0317522384 168 0.12 :(
##  4:     10.5      0.5339286     0.5401276  0.0027661467 168 0.89 :(
##  5:     13.5      0.5071429     0.5174551 -0.0066784780 168 0.74 :(
##  6:     16.5      0.5232143     0.5305272 -0.0054376495 168 0.78 :(
##  7:     19.5      0.4976190     0.5315528 -0.0349803686 168 0.062 .
##  8:     22.5      0.4779762     0.4897264 -0.0103383643 168 0.64 :(
##  9:     25.5      0.4797619     0.4805683  0.0009212402 168 0.95 :(
## 10:     28.5      0.4642857     0.4572889  0.0071690193 168 0.72 :(
##     time    error.diff shapes
##  1:  1.5 -0.0627956484     24
##  2:  4.5 -0.0363491910     16
##  3:  7.5 -0.0317522384     16
##  4: 10.5  0.0027661467     16
##  5: 13.5 -0.0066784780     16
##  6: 16.5 -0.0054376495     16
##  7: 19.5 -0.0349803686     16
##  8: 22.5 -0.0103383643     16
##  9: 25.5  0.0009212402     16
## 10: 28.5  0.0071690193     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4675439     0.5915162 -0.13547480 114 1.9e-05 ***
##  2:      4.5      0.5064327     0.6086245 -0.08890313 171 2.4e-06 ***
##  3:      7.5      0.4643275     0.5299704 -0.06487600 171   0.0023 **
##  4:     10.5      0.5116959     0.5811872 -0.06875274 171 0.00088 ***
##  5:     13.5      0.4719298     0.5608340 -0.08034513 171 1.4e-05 ***
##  6:     16.5      0.4298246     0.5253020 -0.10455036 171 7.9e-06 ***
##  7:     19.5      0.4847953     0.5627322 -0.06849638 171 0.00024 ***
##  8:     22.5      0.4947368     0.5603749 -0.05563539 171   0.0035 **
##  9:     25.5      0.5356725     0.5815067 -0.03269431 171     0.074 .
## 10:     28.5      0.4970760     0.5691664 -0.07055827 171   0.0019 **
##     time  error.diff shapes
##  1:  1.5 -0.13547480     24
##  2:  4.5 -0.08890313     24
##  3:  7.5 -0.06487600     24
##  4: 10.5 -0.06875274     24
##  5: 13.5 -0.08034513     24
##  6: 16.5 -0.10455036     24
##  7: 19.5 -0.06849638     24
##  8: 22.5 -0.05563539     24
##  9: 25.5 -0.03269431     16
## 10: 28.5 -0.07055827     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4412281     0.6044431 -0.170878154 114 1.2e-06 ***
##  2:      4.5      0.5081871     0.6442014 -0.150571723 171 5.3e-08 ***
##  3:      7.5      0.5093567     0.5633809 -0.064995099 171     0.011 *
##  4:     10.5      0.5204678     0.5330653 -0.016026700 171     0.51 :(
##  5:     13.5      0.5157895     0.5198918 -0.009673167 171     0.71 :(
##  6:     16.5      0.5093567     0.4996879  0.003991574 171     0.88 :(
##  7:     19.5      0.4614035     0.4399282  0.012356963 171     0.58 :(
##  8:     22.5      0.4280702     0.4071581  0.015645798 171     0.51 :(
##  9:     25.5      0.4614035     0.3861396  0.082671444 171   0.0019 **
## 10:     28.5      0.4485380     0.3521331  0.085368787 171    0.001 **
##     time   error.diff shapes
##  1:  1.5 -0.170878154     24
##  2:  4.5 -0.150571723     24
##  3:  7.5 -0.064995099     24
##  4: 10.5 -0.016026700     16
##  5: 13.5 -0.009673167     16
##  6: 16.5  0.003991574     16
##  7: 19.5  0.012356963     16
##  8: 22.5  0.015645798     16
##  9: 25.5  0.082671444     24
## 10: 28.5  0.085368787     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74631  -0.17417  -0.06454   0.23463   0.56912  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.28522    0.03182   8.965  < 2e-16 ***
## timeNorm     0.08444    0.03049   2.770  0.00573 ** 
## obj.diff    -0.61929    0.03245 -19.083  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06163681)
## 
##     Null deviance: 77.771  on 869  degrees of freedom
## Residual deviance: 53.439  on 867  degrees of freedom
## AIC: 49.696
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73122  -0.20517   0.00624   0.21924   0.73661  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19286    0.01780  10.834   <2e-16 ***
## timeNorm     0.04068    0.02127   1.913    0.056 .  
## obj.diff    -0.45393    0.01979 -22.933   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06630014)
## 
##     Null deviance: 163.11  on 1913  degrees of freedom
## Residual deviance: 126.70  on 1911  degrees of freedom
## AIC: 242.93
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74697  -0.19411  -0.00423   0.20394   0.71090  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.14668    0.01508   9.724   <2e-16 ***
## timeNorm     0.04018    0.01957   2.053   0.0402 *  
## obj.diff    -0.39929    0.01877 -21.269   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06192699)
## 
##     Null deviance: 162.90  on 2145  degrees of freedom
## Residual deviance: 132.71  on 2143  degrees of freedom
## AIC: 125.34
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5550000     0.7911337 -0.24615978 60 8.9e-08 ***
##  2:      4.5      0.5711111     0.7789270 -0.22600865 90 6.4e-08 ***
##  3:      7.5      0.6122222     0.7692393 -0.17328232 90 2.6e-06 ***
##  4:     10.5      0.6355556     0.7341570 -0.10520584 90   0.0027 **
##  5:     13.5      0.6277778     0.7653763 -0.16821149 90 1.2e-05 ***
##  6:     16.5      0.6155556     0.7325880 -0.13596399 90 0.00023 ***
##  7:     19.5      0.6311111     0.7153889 -0.09339359 90   0.0017 **
##  8:     22.5      0.6188889     0.7245870 -0.10874983 90   0.0029 **
##  9:     25.5      0.6011111     0.6862825 -0.08012592 90     0.027 *
## 10:     28.5      0.6100000     0.6602684 -0.04668910 90     0.17 :(
##     time  error.diff shapes
##  1:  1.5 -0.24615978     24
##  2:  4.5 -0.22600865     24
##  3:  7.5 -0.17328232     24
##  4: 10.5 -0.10520584     24
##  5: 13.5 -0.16821149     24
##  6: 16.5 -0.13596399     24
##  7: 19.5 -0.09339359     24
##  8: 22.5 -0.10874983     24
##  9: 25.5 -0.08012592     24
## 10: 28.5 -0.04668910     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5053030     0.6039107 -0.099983959 132 0.00058 ***
##  2:      4.5      0.5661616     0.6746817 -0.107388316 198 4.2e-07 ***
##  3:      7.5      0.5090909     0.5371689 -0.034806432 198     0.099 .
##  4:     10.5      0.5434343     0.5804839 -0.035366367 198     0.12 :(
##  5:     13.5      0.5287879     0.5665343 -0.035968443 198     0.062 .
##  6:     16.5      0.5156566     0.5499096 -0.039303401 198     0.065 .
##  7:     19.5      0.4959596     0.5544583 -0.060120358 198   0.0038 **
##  8:     22.5      0.4868687     0.5086071 -0.026774034 198      0.2 :(
##  9:     25.5      0.5318182     0.5182748  0.009712085 198     0.68 :(
## 10:     28.5      0.5070707     0.5052319 -0.006835793 198     0.73 :(
##     time   error.diff shapes
##  1:  1.5 -0.099983959     24
##  2:  4.5 -0.107388316     24
##  3:  7.5 -0.034806432     16
##  4: 10.5 -0.035366367     16
##  5: 13.5 -0.035968443     16
##  6: 16.5 -0.039303401     16
##  7: 19.5 -0.060120358     24
##  8: 22.5 -0.026774034     16
##  9: 25.5  0.009712085     16
## 10: 28.5 -0.006835793     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.4290541     0.5165266 -0.083246216 148 0.0015 **
##  2:      4.5      0.4454955     0.4799317 -0.033757771 222   0.081 .
##  3:      7.5      0.4315315     0.4611572 -0.026799425 222   0.15 :(
##  4:     10.5      0.4567568     0.4516607  0.008265497 222   0.66 :(
##  5:     13.5      0.4184685     0.4084635  0.016416425 222   0.44 :(
##  6:     16.5      0.4099099     0.4035442  0.005741364 222   0.79 :(
##  7:     19.5      0.4072072     0.3900362  0.012366817 222   0.52 :(
##  8:     22.5      0.3873874     0.3684916  0.017586402 222   0.32 :(
##  9:     25.5      0.4130631     0.3685548  0.043485459 222   0.013 *
## 10:     28.5      0.3801802     0.3374175  0.035295984 222   0.067 .
##     time   error.diff shapes
##  1:  1.5 -0.083246216     24
##  2:  4.5 -0.033757771     16
##  3:  7.5 -0.026799425     16
##  4: 10.5  0.008265497     16
##  5: 13.5  0.016416425     16
##  6: 16.5  0.005741364     16
##  7: 19.5  0.012366817     16
##  8: 22.5  0.017586402     16
##  9: 25.5  0.043485459     24
## 10: 28.5  0.035295984     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65062  -0.16552  -0.07705   0.21881   0.38387  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29158    0.07860   3.710  0.00026 ***
## timeNorm     0.04078    0.04734   0.861  0.38990    
## obj.diff    -0.58583    0.08967  -6.533 4.12e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.03968196)
## 
##     Null deviance: 10.9054  on 231  degrees of freedom
## Residual deviance:  9.0872  on 229  degrees of freedom
## AIC: -85.263
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.6250000     0.8544830 -0.23067342 16   0.0013 **
##  2:      4.5      0.6375000     0.7995145 -0.16957820 24   0.0048 **
##  3:      7.5      0.6208333     0.7551085 -0.13379284 24     0.012 *
##  4:     10.5      0.6375000     0.7836615 -0.15718140 24   0.0079 **
##  5:     13.5      0.6250000     0.8240112 -0.20576489 24 6.4e-05 ***
##  6:     16.5      0.6375000     0.7818411 -0.15147782 24     0.027 *
##  7:     19.5      0.6541667     0.7263256 -0.07096924 24     0.13 :(
##  8:     22.5      0.6458333     0.7654436 -0.12523757 24     0.046 *
##  9:     25.5      0.6583333     0.7908307 -0.13301969 24   0.0072 **
## 10:     28.5      0.6166667     0.7394768 -0.11097038 24     0.039 *
##     time  error.diff shapes
##  1:  1.5 -0.23067342     24
##  2:  4.5 -0.16957820     24
##  3:  7.5 -0.13379284     24
##  4: 10.5 -0.15718140     24
##  5: 13.5 -0.20576489     24
##  6: 16.5 -0.15147782     24
##  7: 19.5 -0.07096924     16
##  8: 22.5 -0.12523757     24
##  9: 25.5 -0.13301969     24
## 10: 28.5 -0.11097038     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68934  -0.16575   0.00973   0.19104   0.67014  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.157587   0.040226   3.918 9.92e-05 ***
## timeNorm    -0.008747   0.037508  -0.233    0.816    
## obj.diff    -0.364236   0.054058  -6.738 3.61e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06744585)
## 
##     Null deviance: 45.961  on 637  degrees of freedom
## Residual deviance: 42.828  on 635  degrees of freedom
## AIC: 95.236
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5204545     0.6251419 -0.096882522 44   0.034 *
##  2:      4.5      0.5454545     0.6224524 -0.069888163 66   0.042 *
##  3:      7.5      0.5212121     0.5482212 -0.022392160 66   0.54 :(
##  4:     10.5      0.5257576     0.5744464 -0.036347555 66   0.35 :(
##  5:     13.5      0.5348485     0.5455378 -0.006192686 66   0.85 :(
##  6:     16.5      0.5272727     0.5560045 -0.033252815 66   0.35 :(
##  7:     19.5      0.4712121     0.5704673 -0.107605826 66 0.0013 **
##  8:     22.5      0.4439394     0.5060978 -0.066259279 66   0.063 .
##  9:     25.5      0.4787879     0.4999714 -0.024063555 66   0.54 :(
## 10:     28.5      0.4787879     0.5016324 -0.029290994 66   0.31 :(
##     time   error.diff shapes
##  1:  1.5 -0.096882522     24
##  2:  4.5 -0.069888163     24
##  3:  7.5 -0.022392160     16
##  4: 10.5 -0.036347555     16
##  5: 13.5 -0.006192686     16
##  6: 16.5 -0.033252815     16
##  7: 19.5 -0.107605826     24
##  8: 22.5 -0.066259279     16
##  9: 25.5 -0.024063555     16
## 10: 28.5 -0.029290994     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61934  -0.16018   0.01038   0.17385   0.53652  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11181    0.02651   4.217 2.78e-05 ***
## timeNorm     0.02800    0.02850   0.983    0.326    
## obj.diff    -0.19693    0.04083  -4.823 1.71e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04519554)
## 
##     Null deviance: 35.197  on 753  degrees of freedom
## Residual deviance: 33.942  on 751  degrees of freedom
## AIC: -190.2
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5192308     0.5021701  0.020553607 52   0.56 :(
##  2:      4.5      0.4820513     0.4581003  0.028602593 78   0.28 :(
##  3:      7.5      0.4602564     0.4705078 -0.007131425 78   0.76 :(
##  4:     10.5      0.5089744     0.4361551  0.085310624 78 0.0021 **
##  5:     13.5      0.4474359     0.3993679  0.055711847 78   0.043 *
##  6:     16.5      0.4846154     0.4316421  0.056312672 78   0.036 *
##  7:     19.5      0.4717949     0.4386951  0.030866623 78   0.22 :(
##  8:     22.5      0.4551282     0.3910376  0.068335238 78   0.013 *
##  9:     25.5      0.4256410     0.3686849  0.059334781 78   0.014 *
## 10:     28.5      0.4051282     0.3329405  0.069444110 78 0.0055 **
##     time   error.diff shapes
##  1:  1.5  0.020553607     16
##  2:  4.5  0.028602593     16
##  3:  7.5 -0.007131425     16
##  4: 10.5  0.085310624     24
##  5: 13.5  0.055711847     24
##  6: 16.5  0.056312672     24
##  7: 19.5  0.030866623     16
##  8: 22.5  0.068335238     24
##  9: 25.5  0.059334781     24
## 10: 28.5  0.069444110     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73870  -0.20639  -0.03261   0.20631   0.62395  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21907    0.04432   4.943 1.31e-06 ***
## timeNorm     0.04070    0.05278   0.771    0.441    
## obj.diff    -0.51813    0.04443 -11.662  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06278642)
## 
##     Null deviance: 26.637  on 289  degrees of freedom
## Residual deviance: 18.020  on 287  degrees of freedom
## AIC: 25.244
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5200000     0.6406758 -0.14084025 20   0.11 :(
##  2:      4.5      0.5233333     0.6698386 -0.14736319 30   0.023 *
##  3:      7.5      0.5600000     0.7188233 -0.16920919 30 0.0047 **
##  4:     10.5      0.6166667     0.7030218 -0.09216708 30   0.12 :(
##  5:     13.5      0.6300000     0.7358829 -0.09735674 30   0.047 *
##  6:     16.5      0.5033333     0.6317603 -0.17169488 30   0.025 *
##  7:     19.5      0.5666667     0.6733686 -0.14446148 30   0.061 .
##  8:     22.5      0.6766667     0.7281888 -0.04561332 30   0.53 :(
##  9:     25.5      0.5200000     0.6381949 -0.10625786 30    0.07 .
## 10:     28.5      0.5400000     0.6229223 -0.06610116 30   0.28 :(
##     time  error.diff shapes
##  1:  1.5 -0.14084025     16
##  2:  4.5 -0.14736319     24
##  3:  7.5 -0.16920919     24
##  4: 10.5 -0.09216708     16
##  5: 13.5 -0.09735674     24
##  6: 16.5 -0.17169488     24
##  7: 19.5 -0.14446148     16
##  8: 22.5 -0.04561332     16
##  9: 25.5 -0.10625786     16
## 10: 28.5 -0.06610116     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72177  -0.20599   0.01874   0.20085   0.76816  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11023    0.02506   4.398 1.24e-05 ***
## timeNorm     0.04083    0.03333   1.225    0.221    
## obj.diff    -0.31517    0.02602 -12.115  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06764777)
## 
##     Null deviance: 62.813  on 782  degrees of freedom
## Residual deviance: 52.765  on 780  degrees of freedom
## AIC: 118.09
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5185185     0.5861542 -0.078070575 54   0.067 .
##  2:      4.5      0.5839506     0.6542733 -0.051796887 81 0.0091 **
##  3:      7.5      0.4753086     0.4885366 -0.021849678 81    0.5 :(
##  4:     10.5      0.5246914     0.5974452 -0.067630645 81   0.038 *
##  5:     13.5      0.4975309     0.5796225 -0.073998141 81 0.0059 **
##  6:     16.5      0.4765432     0.5246856 -0.047858770 81   0.13 :(
##  7:     19.5      0.5222222     0.5750254 -0.028466125 81    0.2 :(
##  8:     22.5      0.4962963     0.5354099 -0.034256700 81   0.13 :(
##  9:     25.5      0.5827160     0.5863455 -0.002667718 81    0.9 :(
## 10:     28.5      0.5555556     0.5940647 -0.044429134 81   0.14 :(
##     time   error.diff shapes
##  1:  1.5 -0.078070575     16
##  2:  4.5 -0.051796887     24
##  3:  7.5 -0.021849678     16
##  4: 10.5 -0.067630645     24
##  5: 13.5 -0.073998141     24
##  6: 16.5 -0.047858770     16
##  7: 19.5 -0.028466125     16
##  8: 22.5 -0.034256700     16
##  9: 25.5 -0.002667718     16
## 10: 28.5 -0.044429134     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6776  -0.1565  -0.0853   0.2416   0.7578  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06999    0.02734   2.560   0.0107 *  
## timeNorm     0.03858    0.03825   1.009   0.3136    
## obj.diff    -0.38977    0.02922 -13.340   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06601407)
## 
##     Null deviance: 49.907  on 579  degrees of freedom
## Residual deviance: 38.090  on 577  degrees of freedom
## AIC: 74.586
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3725000     0.5741750 -0.21489529 40 0.00021 ***
##  2:      4.5      0.3933333     0.5163915 -0.10929842 60   0.0015 **
##  3:      7.5      0.4016667     0.4914797 -0.07982177 60     0.026 *
##  4:     10.5      0.4416667     0.4983217 -0.06076145 60     0.051 .
##  5:     13.5      0.3583333     0.4479451 -0.07803043 60     0.014 *
##  6:     16.5      0.3300000     0.4729050 -0.14565076 60 0.00013 ***
##  7:     19.5      0.3933333     0.4908181 -0.08289150 60   0.0029 **
##  8:     22.5      0.4016667     0.5101706 -0.10134460 60      0.01 *
##  9:     25.5      0.4800000     0.5466303 -0.04452465 60     0.13 :(
## 10:     28.5      0.3966667     0.5086757 -0.11133031 60   0.0045 **
##     time  error.diff shapes
##  1:  1.5 -0.21489529     24
##  2:  4.5 -0.10929842     24
##  3:  7.5 -0.07982177     24
##  4: 10.5 -0.06076145     16
##  5: 13.5 -0.07803043     24
##  6: 16.5 -0.14565076     24
##  7: 19.5 -0.08289150     24
##  8: 22.5 -0.10134460     24
##  9: 25.5 -0.04452465     16
## 10: 28.5 -0.11133031     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71478  -0.14986  -0.08709   0.27424   0.49224  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.41766    0.06225   6.709 8.03e-11 ***
## timeNorm     0.10877    0.05400   2.014   0.0447 *  
## obj.diff    -0.79794    0.06078 -13.128  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07235681)
## 
##     Null deviance: 40.087  on 347  degrees of freedom
## Residual deviance: 24.963  on 345  degrees of freedom
## AIC: 78.67
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff  n        pval
##  1:      1.5      0.5375000     0.8742824 -0.3400032294 24   6e-07 ***
##  2:      4.5      0.5666667     0.8561091 -0.3102717066 36 6.7e-06 ***
##  3:      7.5      0.6500000     0.8206731 -0.1960451679 36   0.0049 **
##  4:     10.5      0.6500000     0.7270999 -0.0819161707 36     0.19 :(
##  5:     13.5      0.6277778     0.7508644 -0.1701449326 36     0.043 *
##  6:     16.5      0.6944444     0.7837756 -0.1034756273 36     0.098 .
##  7:     19.5      0.6694444     0.7431148 -0.0686429014 36     0.098 .
##  8:     22.5      0.5527778     0.6943478 -0.1472475774 36     0.018 *
##  9:     25.5      0.6305556     0.6566567  0.0008706139 36     0.99 :(
## 10:     28.5      0.6638889     0.6385846  0.0191168357 36     0.67 :(
##     time    error.diff shapes
##  1:  1.5 -0.3400032294     24
##  2:  4.5 -0.3102717066     24
##  3:  7.5 -0.1960451679     24
##  4: 10.5 -0.0819161707     16
##  5: 13.5 -0.1701449326     24
##  6: 16.5 -0.1034756273     16
##  7: 19.5 -0.0686429014     16
##  8: 22.5 -0.1472475774     24
##  9: 25.5  0.0008706139     16
## 10: 28.5  0.0191168357     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61698  -0.09821  -0.02095   0.05412   0.55541  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.423728   0.031809  13.321   <2e-16 ***
## timeNorm     0.003163   0.036698   0.086    0.931    
## obj.diff    -0.802814   0.032859 -24.432   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04686494)
## 
##     Null deviance: 53.857  on 492  degrees of freedom
## Residual deviance: 22.964  on 490  degrees of freedom
## AIC: -104.76
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.4647059     0.6046363 -0.12935792 34   0.016 *
##  2:      4.5      0.5647059     0.7746858 -0.21647631 51 8e-06 ***
##  3:      7.5      0.5470588     0.6001055 -0.07284797 51   0.12 :(
##  4:     10.5      0.5960784     0.5613586  0.01741505 51    0.7 :(
##  5:     13.5      0.5705882     0.5729191 -0.00639387 51   0.84 :(
##  6:     16.5      0.5627451     0.5820839 -0.03055703 51   0.46 :(
##  7:     19.5      0.4862745     0.5010754 -0.01856087 51   0.67 :(
##  8:     22.5      0.5274510     0.4692855  0.06286297 51   0.25 :(
##  9:     25.5      0.5196078     0.4338492  0.08407206 51   0.067 .
## 10:     28.5      0.4666667     0.3688026  0.10634997 51   0.045 *
##     time  error.diff shapes
##  1:  1.5 -0.12935792     24
##  2:  4.5 -0.21647631     24
##  3:  7.5 -0.07284797     16
##  4: 10.5  0.01741505     16
##  5: 13.5 -0.00639387     16
##  6: 16.5 -0.03055703     16
##  7: 19.5 -0.01856087     16
##  8: 22.5  0.06286297     16
##  9: 25.5  0.08407206     16
## 10: 28.5  0.10634997     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64640  -0.21722  -0.01476   0.20979   0.70507  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.14926    0.02655   5.623 2.59e-08 ***
## timeNorm     0.07344    0.03468   2.118   0.0345 *  
## obj.diff    -0.42494    0.03359 -12.652  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06759368)
## 
##     Null deviance: 68.529  on 811  degrees of freedom
## Residual deviance: 54.683  on 809  degrees of freedom
## AIC: 121.63
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.3857143     0.4886803 -0.106804595 56   0.042 *
##  2:      4.5      0.4488095     0.4741611 -0.035984911 84   0.33 :(
##  3:      7.5      0.4261905     0.4308157 -0.008905831 84   0.78 :(
##  4:     10.5      0.4190476     0.4327296 -0.012280159 84   0.73 :(
##  5:     13.5      0.4345238     0.3887083  0.053471734 84    0.2 :(
##  6:     16.5      0.3976190     0.3279099  0.068597536 84   0.045 *
##  7:     19.5      0.3571429     0.2728660  0.079800480 84   0.029 *
##  8:     22.5      0.3142857     0.2463567  0.051170859 84   0.11 :(
##  9:     25.5      0.3535714     0.2412372  0.115593592 84 0.0023 **
## 10:     28.5      0.3452381     0.2192474  0.118100349 84 0.0045 **
##     time   error.diff shapes
##  1:  1.5 -0.106804595     24
##  2:  4.5 -0.035984911     16
##  3:  7.5 -0.008905831     16
##  4: 10.5 -0.012280159     16
##  5: 13.5  0.053471734     16
##  6: 16.5  0.068597536     24
##  7: 19.5  0.079800480     24
##  8: 22.5  0.051170859     16
##  9: 25.5  0.115593592     24
## 10: 28.5  0.118100349     24